Reliability assessment of autonomous vehicles based on the safety control structure

Abstract

The recent social trends and accelerated technological progress culminated in the development of autonomous vehicles (AVs). Reliability assessment for AV systems is in high demand before its market launch. In safety-critical systems (SCSs) such as AV systems, the reliability concept should be broadened to consider more safety-related issues. In this paper, reliability is defined as the probability that the system performs satisfactorily for a given period of time under stated conditions. This paper proposes a reliability assessment framework of AV, consisting of three main stages: (i) modeling the safety control structure through the Systems-Theoretic Accident Model and Processes (STAMP); (ii) mapping the control structure and functional relationships to a directed acyclic graph (DAG); and (iii) construct a Bayesian network (BN) on DAG to assess the system reliability. The fully automated (level 5) vehicle system is shown as a numeric example to illustrate how this suggested framework works. A brief discussion on involving human factors in systems to analyze lower levels of automated vehicles is also included, demonstrating the need for further research on real case studies.

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

“…In brief, the control system is the bridge between the high‐level decision‐making processes and the physical execution of actions in autonomous driving. Its ability to ensure safety, precise manoeuvring, adaptability, and comfortable passenger experiences underscores its critical role in the overall success and adoption of self‐driving vehicles [56].…”

“…Several element reasons highlight the significance of the control phase in autonomous driving such as safety, trajectory tracking, manoeuvre execution, smooth ride, energy efficiency which includes optimal control strategies to improve energy efficiency by optimizing acceleration, the integration with decision making that determines high‐level actions while the control system translates these decisions into physical vehicle/robot movements, and finally, the control phase requires rigorous testing, validation, and verification to ensure its reliability and performance across a wide range of scenarios. Thorough testing helps identify potential issues and refine control algorithms [46–66].…”

Editorial for the special issue on the International Workshop on Autonomous System Safety (IWASS)

“…Wang et al 8 present a framework for assessing the reliability of autonomous vehicles. Reliability and safety are closely related, but complementary system aspects.…”

A refinement and abstraction method of the SPZN formal model for intelligent networked vehicles systems

“…In intelligent networked vehicle systems, formal methods can be used to establish precise mathematical models to describe the states, events, and transition rules of the system. These models can be verified with system-level properties through formal verification techniques such as model checking, theorem proving, and symbolic execution [20][21][22].…”